When we think about the notion of responsible AI, we think about the AI model, we think about regulation, and yet at the forefront of what constitutes responsible AI is responsibility, and more explicitly, leadership. Responsible AI could be understood as Lead AI.
L: Leadership
Leaders should cultivate a deep understanding of AI capabilities and limitations, enabling them to guide their organizations with a blend of human intuition and machine precision as they navigate the complexities of Human and AI co-intelligence. This brings one particular question to the forefront of the discussion: What leadership skills should leaders consider unlearning or relearning to be ready to lead in an AI world?
E: Ethics
Leaders should spearhead conversations on the ethical use of AI, ensuring that technological advancements not undermine but uphold human values. Society is already, and will increasingly be, challenged by new ethical issues. Among the profound AI ethical challenges we face is the phenomenon of people seeking to materialize the 'illusion of presence' of those who have passed away, through AI. How do we balance the comfort provided by these digital recreations with the potential ethical and psychological impacts?
A: Accountability
As organic and inorganic intelligence coexist and cooperate, it becomes crucial to elevate certain rights to the level of universal standards. Among others, these should include the right to AI transparency, where individuals understand how AI systems make decisions that affect their lives, and algorithmic accountability, ensuring AI actions are aligned with societal values. Leaders should advocate for the elevation of these new rights to status of new norms, fostering a culture of trust and responsibility in AI development and deployment.
D: Design
The pervasive use of AI systems, particularly GenAI, leads to the creation of filter bubbles and echo chambers, limiting exposure to diverse perspectives. To mitigate these effects, leaders should promote the design of AI systems that prioritize everyone, ensuring that those systems help create bridges instead of exacerbating divides. This involves developing algorithms that encourage the exploration of varied viewpoints and foster an environment where diverse voices are heard and valued. It also implies governance structures that are flexible and adaptive, not just external to AI systems (e.g., regulations) but also embedded within them leveraging what AI does best: analyzing patterns, and what lies within human agency: determining what is right, to maintain alignment with our collective values.
Ensuring that AI benefits all of society requires more than just advanced AI models and regulations. It hinges on our leadership models first.